Towards better ad experience: Click prediction leveraging sequential networks derived specifically from user search behaviors

Shengzhe Li, Tomoko Izumi, Yu Kuratake, Jiali Yao, Jerry Turner, Daisuke Kawahara, Sadao Kurohashi

Research output: Contribution to conferencePaperpeer-review

Abstract

We propose a sequential modeling approach to improve click prediction for search engine advertising. Unlike previous studies leveraging advertisement content and their relevance-to-query information, we employ only users' search behavioral features such as users' query texts and actual click records of both organic search results and advertisements. By leveraging long short-term memory (LSTM) networks, we successfully modeled users' sequential search behaviors and fully utilized them in click predictions. Through experiments conducted with large-scale search log data obtained from an actual commercial search engine, we demonstrated that our method combining users' current and previous search behaviors reaches better prediction performance than baseline methods.

Original languageEnglish
Pages461-470
Number of pages10
Publication statusPublished - 2019
Externally publishedYes
Event33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019 - Hakodate, Japan
Duration: 2019 Sep 132019 Sep 15

Conference

Conference33rd Pacific Asia Conference on Language, Information and Computation, PACLIC 2019
Country/TerritoryJapan
CityHakodate
Period19/9/1319/9/15

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

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